Overview

Dataset statistics

Number of variables70
Number of observations3669
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 MiB
Average record size in memory560.0 B

Variable types

Categorical51
Unsupported2
Numeric17

Alerts

MagicNumber has constant value ""Constant
HeaderType has constant value ""Constant
SubChannelNumber has constant value ""Constant
NumChansToFollow has constant value ""Constant
NumBytesThisRecord has constant value ""Constant
Year has constant value ""Constant
Month has constant value ""Constant
Day has constant value ""Constant
Hour has constant value ""Constant
JulianDay has constant value ""Constant
EventNumber has constant value ""Constant
SoundVelocity has constant value ""Constant
WaterTemperature has constant value ""Constant
Pressure has constant value ""Constant
ComputedSoundVelocity has constant value ""Constant
MagX has constant value ""Constant
MagY has constant value ""Constant
MagZ has constant value ""Constant
AuxVal1 has constant value ""Constant
AuxVal2 has constant value ""Constant
AuxVal3 has constant value ""Constant
AuxVal4 has constant value ""Constant
AuxVal5 has constant value ""Constant
AuxVal6 has constant value ""Constant
SpeedLog has constant value ""Constant
Turbidity has constant value ""Constant
ShipDepth has constant value ""Constant
SonarStatus has constant value ""Constant
RangeToFish has constant value ""Constant
BearingToFish has constant value ""Constant
CableOut has constant value ""Constant
Layback has constant value ""Constant
CableTension has constant value ""Constant
SensorDepth has constant value ""Constant
SensorAuxAltitude has constant value ""Constant
SensorPitch has constant value ""Constant
SensorRoll has constant value ""Constant
Heave has constant value ""Constant
Yaw has constant value ""Constant
ComputerClockHour has constant value ""Constant
ComputerClockMinute has constant value ""Constant
ComputerClockSecond has constant value ""Constant
ComputerClockHsec has constant value ""Constant
FishPositionDeltaX has constant value ""Constant
FishPositionDeltaY has constant value ""Constant
FishPositionErrorCode has constant value ""Constant
OptionalOffset has constant value ""Constant
CableOutHundredths has constant value ""Constant
Second is highly overall correlated with FixTimeSecondHigh correlation
PingNumber is highly overall correlated with ShipGyro and 11 other fieldsHigh correlation
ShipSpeed is highly overall correlated with SensorSpeed and 2 other fieldsHigh correlation
ShipGyro is highly overall correlated with PingNumber and 12 other fieldsHigh correlation
ShipYcoordinate is highly overall correlated with PingNumber and 11 other fieldsHigh correlation
ShipXcoordinate is highly overall correlated with PingNumber and 11 other fieldsHigh correlation
ShipAltitude is highly overall correlated with PingNumber and 11 other fieldsHigh correlation
FixTimeSecond is highly overall correlated with SecondHigh correlation
FixTimeHsecond is highly overall correlated with FixTimeHour and 1 other fieldsHigh correlation
SensorSpeed is highly overall correlated with ShipSpeed and 2 other fieldsHigh correlation
SensorYcoordinate is highly overall correlated with PingNumber and 11 other fieldsHigh correlation
SensorXcoordinate is highly overall correlated with PingNumber and 11 other fieldsHigh correlation
SensorPrimaryAltitude is highly overall correlated with PingNumber and 11 other fieldsHigh correlation
SensorHeading is highly overall correlated with PingNumber and 12 other fieldsHigh correlation
AttitudeTimeTag is highly overall correlated with PingNumber and 11 other fieldsHigh correlation
NavFixMilliseconds is highly overall correlated with PingNumber and 11 other fieldsHigh correlation
Minute is highly overall correlated with PingNumber and 6 other fieldsHigh correlation
FixTimeHour is highly overall correlated with ShipSpeed and 10 other fieldsHigh correlation
FixTimeMinute is highly overall correlated with PingNumber and 15 other fieldsHigh correlation
FixTimeHour is highly imbalanced (93.7%)Imbalance
PingNumber is uniformly distributedUniform
PingNumber has unique valuesUnique
Reserved1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
ReservedSpace2 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Second has 54 (1.5%) zerosZeros
HSeconds has 37 (1.0%) zerosZeros
FixTimeSecond has 82 (2.2%) zerosZeros

Reproduction

Analysis started2023-08-28 14:15:16.013334
Analysis finished2023-08-28 14:16:16.661541
Duration1 minute and 0.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

MagicNumber
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
64206
3669 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters18345
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row64206
2nd row64206
3rd row64206
4th row64206
5th row64206

Common Values

ValueCountFrequency (%)
64206 3669
100.0%

Length

2023-08-28T19:46:16.830210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:17.043109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
64206 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
6 7338
40.0%
4 3669
20.0%
2 3669
20.0%
0 3669
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18345
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 7338
40.0%
4 3669
20.0%
2 3669
20.0%
0 3669
20.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 7338
40.0%
4 3669
20.0%
2 3669
20.0%
0 3669
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 7338
40.0%
4 3669
20.0%
2 3669
20.0%
0 3669
20.0%

HeaderType
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0
3669 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3669
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3669
100.0%

Length

2023-08-28T19:46:17.244368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:17.432092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3669
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3669
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3669
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3669
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3669
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3669
100.0%

SubChannelNumber
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0
3669 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3669
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3669
100.0%

Length

2023-08-28T19:46:17.632624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:17.815480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3669
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3669
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3669
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3669
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3669
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3669
100.0%

NumChansToFollow
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
2
3669 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3669
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 3669
100.0%

Length

2023-08-28T19:46:18.296643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:18.489460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
2 3669
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3669
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3669
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 3669
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3669
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 3669
100.0%

Reserved1
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size28.8 KiB

NumBytesThisRecord
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
2384
3669 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters14676
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2384
2nd row2384
3rd row2384
4th row2384
5th row2384

Common Values

ValueCountFrequency (%)
2384 3669
100.0%

Length

2023-08-28T19:46:18.717512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:18.905409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2384 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
2 3669
25.0%
3 3669
25.0%
8 3669
25.0%
4 3669
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14676
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 3669
25.0%
3 3669
25.0%
8 3669
25.0%
4 3669
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14676
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 3669
25.0%
3 3669
25.0%
8 3669
25.0%
4 3669
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14676
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 3669
25.0%
3 3669
25.0%
8 3669
25.0%
4 3669
25.0%

Year
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
2020
3669 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters14676
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 3669
100.0%

Length

2023-08-28T19:46:19.118540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:19.300355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
2 7338
50.0%
0 7338
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14676
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 7338
50.0%
0 7338
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14676
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 7338
50.0%
0 7338
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14676
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 7338
50.0%
0 7338
50.0%

Month
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
7
3669 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3669
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7
2nd row7
3rd row7
4th row7
5th row7

Common Values

ValueCountFrequency (%)
7 3669
100.0%

Length

2023-08-28T19:46:19.500052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:19.691567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
7 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
7 3669
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3669
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3669
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
7 3669
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3669
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 3669
100.0%

Day
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
24
3669 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters7338
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row24
2nd row24
3rd row24
4th row24
5th row24

Common Values

ValueCountFrequency (%)
24 3669
100.0%

Length

2023-08-28T19:46:19.884026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:20.055807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
24 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
2 3669
50.0%
4 3669
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7338
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 3669
50.0%
4 3669
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7338
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 3669
50.0%
4 3669
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7338
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 3669
50.0%
4 3669
50.0%

Hour
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
19
3669 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters7338
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row19
2nd row19
3rd row19
4th row19
5th row19

Common Values

ValueCountFrequency (%)
19 3669
100.0%

Length

2023-08-28T19:46:20.245885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:20.411038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
19 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
1 3669
50.0%
9 3669
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7338
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3669
50.0%
9 3669
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7338
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3669
50.0%
9 3669
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7338
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3669
50.0%
9 3669
50.0%

Minute
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
14
1639 
15
1509 
13
521 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters7338
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row13
2nd row13
3rd row13
4th row13
5th row13

Common Values

ValueCountFrequency (%)
14 1639
44.7%
15 1509
41.1%
13 521
 
14.2%

Length

2023-08-28T19:46:20.598539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:20.789834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
14 1639
44.7%
15 1509
41.1%
13 521
 
14.2%

Most occurring characters

ValueCountFrequency (%)
1 3669
50.0%
4 1639
22.3%
5 1509
20.6%
3 521
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7338
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3669
50.0%
4 1639
22.3%
5 1509
20.6%
3 521
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Common 7338
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3669
50.0%
4 1639
22.3%
5 1509
20.6%
3 521
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7338
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3669
50.0%
4 1639
22.3%
5 1509
20.6%
3 521
 
7.1%

Second
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct60
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.430908
Minimum0
Maximum59
Zeros54
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-08-28T19:46:21.021408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q116
median33
Q347
95-th percentile56
Maximum59
Range59
Interquartile range (IQR)31

Descriptive statistics

Standard deviation17.352303
Coefficient of variation (CV)0.55207771
Kurtosis-1.2262284
Mean31.430908
Median Absolute Deviation (MAD)15
Skewness-0.19571034
Sum115320
Variance301.10244
MonotonicityNot monotonic
2023-08-28T19:46:21.283052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 111
 
3.0%
49 83
 
2.3%
54 83
 
2.3%
52 83
 
2.3%
46 83
 
2.3%
50 83
 
2.3%
43 82
 
2.2%
47 82
 
2.2%
44 82
 
2.2%
48 81
 
2.2%
Other values (50) 2816
76.8%
ValueCountFrequency (%)
0 54
1.5%
1 56
1.5%
2 54
1.5%
3 54
1.5%
4 55
1.5%
5 55
1.5%
6 55
1.5%
7 53
1.4%
8 56
1.5%
9 54
1.5%
ValueCountFrequency (%)
59 54
1.5%
58 55
1.5%
57 55
1.5%
56 54
1.5%
55 60
1.6%
54 83
2.3%
53 81
2.2%
52 83
2.3%
51 81
2.2%
50 83
2.3%

HSeconds
Real number (ℝ)

ZEROS 

Distinct100
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.49196
Minimum0
Maximum99
Zeros37
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-08-28T19:46:21.548978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q125
median49
Q374
95-th percentile94
Maximum99
Range99
Interquartile range (IQR)49

Descriptive statistics

Standard deviation28.754892
Coefficient of variation (CV)0.58100127
Kurtosis-1.1882887
Mean49.49196
Median Absolute Deviation (MAD)25
Skewness0.0036792061
Sum181586
Variance826.84379
MonotonicityNot monotonic
2023-08-28T19:46:21.800878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 71
 
1.9%
51 50
 
1.4%
69 45
 
1.2%
13 45
 
1.2%
59 44
 
1.2%
97 44
 
1.2%
37 44
 
1.2%
39 43
 
1.2%
75 43
 
1.2%
81 43
 
1.2%
Other values (90) 3197
87.1%
ValueCountFrequency (%)
0 37
1.0%
1 32
0.9%
2 38
1.0%
3 41
1.1%
4 34
0.9%
5 39
1.1%
6 37
1.0%
7 34
0.9%
8 35
1.0%
9 39
1.1%
ValueCountFrequency (%)
99 30
0.8%
98 35
1.0%
97 44
1.2%
96 33
0.9%
95 40
1.1%
94 38
1.0%
93 38
1.0%
92 35
1.0%
91 38
1.0%
90 38
1.0%

JulianDay
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
206
3669 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11007
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row206
2nd row206
3rd row206
4th row206
5th row206

Common Values

ValueCountFrequency (%)
206 3669
100.0%

Length

2023-08-28T19:46:22.035424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:22.207321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
206 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
2 3669
33.3%
0 3669
33.3%
6 3669
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11007
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 3669
33.3%
0 3669
33.3%
6 3669
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 11007
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 3669
33.3%
0 3669
33.3%
6 3669
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 3669
33.3%
0 3669
33.3%
6 3669
33.3%

EventNumber
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0
3669 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3669
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3669
100.0%

Length

2023-08-28T19:46:22.428994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:22.629554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3669
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3669
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3669
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3669
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3669
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3669
100.0%

PingNumber
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct3669
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1834
Minimum0
Maximum3668
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-08-28T19:46:22.836005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile183.4
Q1917
median1834
Q32751
95-th percentile3484.6
Maximum3668
Range3668
Interquartile range (IQR)1834

Descriptive statistics

Standard deviation1059.2934
Coefficient of variation (CV)0.57758637
Kurtosis-1.2
Mean1834
Median Absolute Deviation (MAD)917
Skewness0
Sum6728946
Variance1122102.5
MonotonicityStrictly increasing
2023-08-28T19:46:23.183069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
2438 1
 
< 0.1%
2440 1
 
< 0.1%
2441 1
 
< 0.1%
2442 1
 
< 0.1%
2443 1
 
< 0.1%
2444 1
 
< 0.1%
2445 1
 
< 0.1%
2446 1
 
< 0.1%
2447 1
 
< 0.1%
Other values (3659) 3659
99.7%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
3668 1
< 0.1%
3667 1
< 0.1%
3666 1
< 0.1%
3665 1
< 0.1%
3664 1
< 0.1%
3663 1
< 0.1%
3662 1
< 0.1%
3661 1
< 0.1%
3660 1
< 0.1%
3659 1
< 0.1%

SoundVelocity
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
737.5
3669 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters18345
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row737.5
2nd row737.5
3rd row737.5
4th row737.5
5th row737.5

Common Values

ValueCountFrequency (%)
737.5 3669
100.0%

Length

2023-08-28T19:46:23.480018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:23.677537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
737.5 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
7 7338
40.0%
3 3669
20.0%
. 3669
20.0%
5 3669
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14676
80.0%
Other Punctuation 3669
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 7338
50.0%
3 3669
25.0%
5 3669
25.0%
Other Punctuation
ValueCountFrequency (%)
. 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
7 7338
40.0%
3 3669
20.0%
. 3669
20.0%
5 3669
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 7338
40.0%
3 3669
20.0%
. 3669
20.0%
5 3669
20.0%

WaterTemperature
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0.0
3669 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11007
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3669
100.0%

Length

2023-08-28T19:46:23.884420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:24.056098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7338
66.7%
Other Punctuation 3669
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7338
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11007
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Pressure
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0.0
3669 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11007
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3669
100.0%

Length

2023-08-28T19:46:24.242382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:24.411244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7338
66.7%
Other Punctuation 3669
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7338
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11007
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

ComputedSoundVelocity
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0.0
3669 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11007
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3669
100.0%

Length

2023-08-28T19:46:24.619019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:24.855572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7338
66.7%
Other Punctuation 3669
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7338
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11007
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

MagX
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0.0
3669 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11007
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3669
100.0%

Length

2023-08-28T19:46:25.050318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:25.234538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7338
66.7%
Other Punctuation 3669
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7338
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11007
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

MagY
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0.0
3669 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11007
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3669
100.0%

Length

2023-08-28T19:46:25.412977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:25.593428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7338
66.7%
Other Punctuation 3669
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7338
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11007
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

MagZ
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0.0
3669 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11007
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3669
100.0%

Length

2023-08-28T19:46:25.770337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:25.942319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7338
66.7%
Other Punctuation 3669
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7338
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11007
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

AuxVal1
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0.0
3669 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11007
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3669
100.0%

Length

2023-08-28T19:46:26.114376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:26.301218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7338
66.7%
Other Punctuation 3669
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7338
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11007
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

AuxVal2
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0.0
3669 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11007
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3669
100.0%

Length

2023-08-28T19:46:26.472768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:26.657306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7338
66.7%
Other Punctuation 3669
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7338
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11007
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

AuxVal3
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0.0
3669 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11007
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3669
100.0%

Length

2023-08-28T19:46:26.833256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:27.005384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7338
66.7%
Other Punctuation 3669
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7338
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11007
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

AuxVal4
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0.0
3669 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11007
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3669
100.0%

Length

2023-08-28T19:46:27.189613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:27.355922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7338
66.7%
Other Punctuation 3669
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7338
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11007
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

AuxVal5
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0.0
3669 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11007
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3669
100.0%

Length

2023-08-28T19:46:27.544591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:27.727257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7338
66.7%
Other Punctuation 3669
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7338
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11007
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

AuxVal6
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0.0
3669 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11007
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3669
100.0%

Length

2023-08-28T19:46:27.920520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:28.102973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7338
66.7%
Other Punctuation 3669
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7338
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11007
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

SpeedLog
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0.0
3669 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11007
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3669
100.0%

Length

2023-08-28T19:46:28.296517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:28.476332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7338
66.7%
Other Punctuation 3669
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7338
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11007
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Turbidity
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0.0
3669 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11007
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3669
100.0%

Length

2023-08-28T19:46:28.916552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:29.087295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7338
66.7%
Other Punctuation 3669
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7338
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11007
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

ShipSpeed
Real number (ℝ)

HIGH CORRELATION 

Distinct101
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.237273
Minimum0
Maximum4.8899999
Zeros27
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-08-28T19:46:29.290903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.03
Q12.96
median3.3499999
Q33.6199999
95-th percentile4.0500002
Maximum4.8899999
Range4.8899999
Interquartile range (IQR)0.65999985

Descriptive statistics

Standard deviation0.63851162
Coefficient of variation (CV)0.19723749
Kurtosis4.710968
Mean3.237273
Median Absolute Deviation (MAD)0.3499999
Skewness-1.34103
Sum11877.555
Variance0.40769709
MonotonicityNot monotonic
2023-08-28T19:46:29.548509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 109
 
3.0%
3.920000076 85
 
2.3%
3.49000001 85
 
2.3%
3.670000076 84
 
2.3%
3.400000095 84
 
2.3%
3.440000057 82
 
2.2%
3.420000076 72
 
2.0%
3.609999895 71
 
1.9%
3.619999886 66
 
1.8%
2.960000038 57
 
1.6%
Other values (91) 2874
78.3%
ValueCountFrequency (%)
0 27
0.7%
1.24000001 29
0.8%
1.580000043 27
0.7%
1.669999957 27
0.7%
1.730000019 28
0.8%
1.950000048 28
0.8%
2.029999971 27
0.7%
2.069999933 28
0.8%
2.420000076 27
0.7%
2.430000067 10
 
0.3%
ValueCountFrequency (%)
4.889999866 28
0.8%
4.460000038 27
0.7%
4.380000114 28
0.8%
4.179999828 27
0.7%
4.170000076 28
0.8%
4.130000114 28
0.8%
4.050000191 27
0.7%
3.980000019 28
0.8%
3.970000029 41
1.1%
3.960000038 56
1.5%

ShipGyro
Real number (ℝ)

HIGH CORRELATION 

Distinct134
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.41506
Minimum0
Maximum175.64999
Zeros27
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-08-28T19:46:29.898479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile72.519997
Q188.730003
median98.879997
Q3115.46
95-th percentile156.03999
Maximum175.64999
Range175.64999
Interquartile range (IQR)26.729996

Descriptive statistics

Standard deviation25.862353
Coefficient of variation (CV)0.24768797
Kurtosis1.7597051
Mean104.41506
Median Absolute Deviation (MAD)12.68
Skewness0.26557849
Sum383098.85
Variance668.86132
MonotonicityNot monotonic
2023-08-28T19:46:30.173832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84.08999634 55
 
1.5%
86.19999695 43
 
1.2%
104.0899963 41
 
1.1%
116.3099976 39
 
1.1%
90.16000366 39
 
1.1%
144.3399963 30
 
0.8%
145.6000061 30
 
0.8%
92.23999786 29
 
0.8%
115.2399979 29
 
0.8%
119.5299988 29
 
0.8%
Other values (124) 3305
90.1%
ValueCountFrequency (%)
0 27
0.7%
39.90999985 28
0.8%
59.40000153 27
0.7%
70.76000214 27
0.7%
71.15000153 28
0.8%
71.54000092 27
0.7%
72.51999664 28
0.8%
76.44000244 27
0.7%
76.61000061 28
0.8%
77.94999695 29
0.8%
ValueCountFrequency (%)
175.6499939 29
0.8%
170.1100006 28
0.8%
166.2899933 27
0.7%
158.7899933 28
0.8%
157.8899994 27
0.7%
156.1300049 28
0.8%
156.0399933 27
0.7%
154.5399933 27
0.7%
152.4199982 28
0.8%
150.8099976 28
0.8%

ShipYcoordinate
Real number (ℝ)

HIGH CORRELATION 

Distinct134
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.491227
Minimum0
Maximum46.836034
Zeros27
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-08-28T19:46:30.430615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile46.835537
Q146.83581
median46.83594
Q346.836004
95-th percentile46.836031
Maximum46.836034
Range46.836034
Interquartile range (IQR)0.00019384237

Descriptive statistics

Standard deviation4.003521
Coefficient of variation (CV)0.086113473
Kurtosis131.0765
Mean46.491227
Median Absolute Deviation (MAD)7.9061619 × 10-5
Skewness-11.532781
Sum170576.31
Variance16.02818
MonotonicityNot monotonic
2023-08-28T19:46:30.694770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.83594596 43
 
1.2%
46.83598498 42
 
1.1%
46.83550735 41
 
1.1%
46.83595716 39
 
1.1%
46.83565364 30
 
0.8%
46.83575646 30
 
0.8%
46.83590313 29
 
0.8%
46.83592661 29
 
0.8%
46.83578005 29
 
0.8%
46.83553072 29
 
0.8%
Other values (124) 3328
90.7%
ValueCountFrequency (%)
0 27
0.7%
46.83549907 10
 
0.3%
46.83550735 41
1.1%
46.83551433 14
 
0.4%
46.83551987 28
0.8%
46.835524 27
0.7%
46.83553072 29
0.8%
46.83553734 27
0.7%
46.83554399 28
0.8%
46.83555251 27
0.7%
ValueCountFrequency (%)
46.83603401 28
0.8%
46.83603376 14
0.4%
46.83603341 28
0.8%
46.83603217 27
0.7%
46.83603208 28
0.8%
46.83603196 27
0.7%
46.83603169 28
0.8%
46.83603071 28
0.8%
46.83602854 27
0.7%
46.83602561 28
0.8%

ShipXcoordinate
Real number (ℝ)

HIGH CORRELATION 

Distinct134
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-70.664647
Minimum-71.189895
Maximum0
Zeros27
Zeros (%)0.7%
Negative3642
Negative (%)99.3%
Memory size28.8 KiB
2023-08-28T19:46:30.947818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-71.189895
5-th percentile-71.18978
Q1-71.189235
median-71.188494
Q3-71.187732
95-th percentile-71.187358
Maximum0
Range71.189895
Interquartile range (IQR)0.0015032046

Descriptive statistics

Standard deviation6.0851782
Coefficient of variation (CV)-0.086113474
Kurtosis131.07649
Mean-70.664647
Median Absolute Deviation (MAD)0.00076161822
Skewness11.532781
Sum-259268.59
Variance37.029394
MonotonicityNot monotonic
2023-08-28T19:46:31.238829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-71.18854061 43
 
1.2%
-71.1898945 42
 
1.1%
-71.1873144 41
 
1.1%
-71.1886346 39
 
1.1%
-71.18744095 30
 
0.8%
-71.18754691 30
 
0.8%
-71.18810467 29
 
0.8%
-71.18833045 29
 
0.8%
-71.18758472 29
 
0.8%
-71.18735268 29
 
0.8%
Other values (124) 3328
90.7%
ValueCountFrequency (%)
-71.1898945 42
1.1%
-71.18987567 28
0.8%
-71.18985575 28
0.8%
-71.18983677 28
0.8%
-71.18981795 27
0.7%
-71.18979938 28
0.8%
-71.18977958 27
0.7%
-71.18975933 29
0.8%
-71.18973907 27
0.7%
-71.18971801 28
0.8%
ValueCountFrequency (%)
0 27
0.7%
-71.18730475 10
 
0.3%
-71.1873144 41
1.1%
-71.18732698 14
 
0.4%
-71.18733866 28
0.8%
-71.18734707 27
0.7%
-71.18735268 29
0.8%
-71.18735783 27
0.7%
-71.18736323 28
0.8%
-71.18736809 27
0.7%

ShipAltitude
Real number (ℝ)

HIGH CORRELATION 

Distinct143
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.695557
Minimum16
Maximum159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-08-28T19:46:31.501534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile32
Q140
median46
Q386
95-th percentile153
Maximum159
Range143
Interquartile range (IQR)46

Descriptive statistics

Standard deviation39.001779
Coefficient of variation (CV)0.59367452
Kurtosis-0.058463442
Mean65.695557
Median Absolute Deviation (MAD)7
Skewness1.1925996
Sum241037
Variance1521.1388
MonotonicityNot monotonic
2023-08-28T19:46:31.751601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 214
 
5.8%
41 206
 
5.6%
47 196
 
5.3%
46 172
 
4.7%
45 164
 
4.5%
44 143
 
3.9%
42 137
 
3.7%
39 125
 
3.4%
48 120
 
3.3%
43 117
 
3.2%
Other values (133) 2075
56.6%
ValueCountFrequency (%)
16 1
 
< 0.1%
17 1
 
< 0.1%
18 1
 
< 0.1%
19 3
 
0.1%
20 2
 
0.1%
22 4
 
0.1%
23 20
0.5%
24 15
0.4%
25 15
0.4%
26 13
0.4%
ValueCountFrequency (%)
159 5
 
0.1%
158 5
 
0.1%
157 5
 
0.1%
156 14
 
0.4%
155 62
1.7%
154 63
1.7%
153 55
1.5%
152 13
 
0.4%
151 10
 
0.3%
150 7
 
0.2%

ShipDepth
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0
3669 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3669
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3669
100.0%

Length

2023-08-28T19:46:32.000020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:32.172998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3669
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3669
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3669
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3669
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3669
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3669
100.0%

FixTimeHour
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
19
3642 
0
 
27

Length

Max length2
Median length2
Mean length1.992641
Min length1

Characters and Unicode

Total characters7311
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
19 3642
99.3%
0 27
 
0.7%

Length

2023-08-28T19:46:32.362118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:32.543657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
19 3642
99.3%
0 27
 
0.7%

Most occurring characters

ValueCountFrequency (%)
1 3642
49.8%
9 3642
49.8%
0 27
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7311
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3642
49.8%
9 3642
49.8%
0 27
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 7311
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3642
49.8%
9 3642
49.8%
0 27
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7311
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3642
49.8%
9 3642
49.8%
0 27
 
0.4%

FixTimeMinute
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
14
1635 
15
1493 
13
514 
0
 
27

Length

Max length2
Median length2
Mean length1.992641
Min length1

Characters and Unicode

Total characters7311
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
14 1635
44.6%
15 1493
40.7%
13 514
 
14.0%
0 27
 
0.7%

Length

2023-08-28T19:46:32.739797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:32.939825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
14 1635
44.6%
15 1493
40.7%
13 514
 
14.0%
0 27
 
0.7%

Most occurring characters

ValueCountFrequency (%)
1 3642
49.8%
4 1635
22.4%
5 1493
20.4%
3 514
 
7.0%
0 27
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7311
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3642
49.8%
4 1635
22.4%
5 1493
20.4%
3 514
 
7.0%
0 27
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 7311
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3642
49.8%
4 1635
22.4%
5 1493
20.4%
3 514
 
7.0%
0 27
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7311
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3642
49.8%
4 1635
22.4%
5 1493
20.4%
3 514
 
7.0%
0 27
 
0.4%

FixTimeSecond
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct60
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.095939
Minimum0
Maximum59
Zeros82
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-08-28T19:46:33.174898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q116
median33
Q346
95-th percentile56
Maximum59
Range59
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.498047
Coefficient of variation (CV)0.56271163
Kurtosis-1.2260177
Mean31.095939
Median Absolute Deviation (MAD)15
Skewness-0.18668382
Sum114091
Variance306.18163
MonotonicityNot monotonic
2023-08-28T19:46:33.425796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 108
 
2.9%
53 97
 
2.6%
46 97
 
2.6%
49 87
 
2.4%
51 84
 
2.3%
43 83
 
2.3%
45 83
 
2.3%
0 82
 
2.2%
50 82
 
2.2%
48 81
 
2.2%
Other values (50) 2785
75.9%
ValueCountFrequency (%)
0 82
2.2%
1 56
1.5%
2 54
1.5%
3 56
1.5%
4 55
1.5%
5 57
1.6%
6 54
1.5%
7 56
1.5%
8 55
1.5%
9 56
1.5%
ValueCountFrequency (%)
59 57
1.6%
58 54
1.5%
57 56
1.5%
56 54
1.5%
55 57
1.6%
54 65
1.8%
53 97
2.6%
52 68
1.9%
51 84
2.3%
50 82
2.2%

FixTimeHsecond
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.772963
Minimum0
Maximum95
Zeros27
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-08-28T19:46:33.694376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile43
Q152
median63
Q376
95-th percentile87
Maximum95
Range95
Interquartile range (IQR)24

Descriptive statistics

Standard deviation15.180358
Coefficient of variation (CV)0.23803752
Kurtosis0.83470764
Mean63.772963
Median Absolute Deviation (MAD)12
Skewness-0.38143325
Sum233983
Variance230.44326
MonotonicityNot monotonic
2023-08-28T19:46:33.932678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 165
 
4.5%
46 149
 
4.1%
60 137
 
3.7%
79 128
 
3.5%
52 114
 
3.1%
59 112
 
3.1%
74 111
 
3.0%
70 110
 
3.0%
55 110
 
3.0%
62 86
 
2.3%
Other values (46) 2447
66.7%
ValueCountFrequency (%)
0 27
 
0.7%
37 41
 
1.1%
38 15
 
0.4%
40 41
 
1.1%
41 42
 
1.1%
42 13
 
0.4%
43 14
 
0.4%
44 70
1.9%
45 83
2.3%
46 149
4.1%
ValueCountFrequency (%)
95 12
 
0.3%
93 43
1.2%
91 40
1.1%
90 30
0.8%
89 13
 
0.4%
88 38
1.0%
87 54
1.5%
86 72
2.0%
85 42
1.1%
84 28
 
0.8%

SensorSpeed
Real number (ℝ)

HIGH CORRELATION 

Distinct101
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.237273
Minimum0
Maximum4.8899999
Zeros27
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-08-28T19:46:34.183823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.03
Q12.96
median3.3499999
Q33.6199999
95-th percentile4.0500002
Maximum4.8899999
Range4.8899999
Interquartile range (IQR)0.65999985

Descriptive statistics

Standard deviation0.63851162
Coefficient of variation (CV)0.19723749
Kurtosis4.710968
Mean3.237273
Median Absolute Deviation (MAD)0.3499999
Skewness-1.34103
Sum11877.555
Variance0.40769709
MonotonicityNot monotonic
2023-08-28T19:46:34.444269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 109
 
3.0%
3.920000076 85
 
2.3%
3.49000001 85
 
2.3%
3.670000076 84
 
2.3%
3.400000095 84
 
2.3%
3.440000057 82
 
2.2%
3.420000076 72
 
2.0%
3.609999895 71
 
1.9%
3.619999886 66
 
1.8%
2.960000038 57
 
1.6%
Other values (91) 2874
78.3%
ValueCountFrequency (%)
0 27
0.7%
1.24000001 29
0.8%
1.580000043 27
0.7%
1.669999957 27
0.7%
1.730000019 28
0.8%
1.950000048 28
0.8%
2.029999971 27
0.7%
2.069999933 28
0.8%
2.420000076 27
0.7%
2.430000067 10
 
0.3%
ValueCountFrequency (%)
4.889999866 28
0.8%
4.460000038 27
0.7%
4.380000114 28
0.8%
4.179999828 27
0.7%
4.170000076 28
0.8%
4.130000114 28
0.8%
4.050000191 27
0.7%
3.980000019 28
0.8%
3.970000029 41
1.1%
3.960000038 56
1.5%

SensorYcoordinate
Real number (ℝ)

HIGH CORRELATION 

Distinct134
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.491227
Minimum0
Maximum46.836034
Zeros27
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-08-28T19:46:34.700905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile46.835537
Q146.83581
median46.83594
Q346.836004
95-th percentile46.836031
Maximum46.836034
Range46.836034
Interquartile range (IQR)0.00019384237

Descriptive statistics

Standard deviation4.003521
Coefficient of variation (CV)0.086113473
Kurtosis131.0765
Mean46.491227
Median Absolute Deviation (MAD)7.9061619 × 10-5
Skewness-11.532781
Sum170576.31
Variance16.02818
MonotonicityNot monotonic
2023-08-28T19:46:34.956540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.83594596 43
 
1.2%
46.83598498 42
 
1.1%
46.83550735 41
 
1.1%
46.83595716 39
 
1.1%
46.83565364 30
 
0.8%
46.83575646 30
 
0.8%
46.83590313 29
 
0.8%
46.83592661 29
 
0.8%
46.83578005 29
 
0.8%
46.83553072 29
 
0.8%
Other values (124) 3328
90.7%
ValueCountFrequency (%)
0 27
0.7%
46.83549907 10
 
0.3%
46.83550735 41
1.1%
46.83551433 14
 
0.4%
46.83551987 28
0.8%
46.835524 27
0.7%
46.83553072 29
0.8%
46.83553734 27
0.7%
46.83554399 28
0.8%
46.83555251 27
0.7%
ValueCountFrequency (%)
46.83603401 28
0.8%
46.83603376 14
0.4%
46.83603341 28
0.8%
46.83603217 27
0.7%
46.83603208 28
0.8%
46.83603196 27
0.7%
46.83603169 28
0.8%
46.83603071 28
0.8%
46.83602854 27
0.7%
46.83602561 28
0.8%

SensorXcoordinate
Real number (ℝ)

HIGH CORRELATION 

Distinct134
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-70.664647
Minimum-71.189895
Maximum0
Zeros27
Zeros (%)0.7%
Negative3642
Negative (%)99.3%
Memory size28.8 KiB
2023-08-28T19:46:35.206611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-71.189895
5-th percentile-71.18978
Q1-71.189235
median-71.188494
Q3-71.187732
95-th percentile-71.187358
Maximum0
Range71.189895
Interquartile range (IQR)0.0015032046

Descriptive statistics

Standard deviation6.0851782
Coefficient of variation (CV)-0.086113474
Kurtosis131.07649
Mean-70.664647
Median Absolute Deviation (MAD)0.00076161822
Skewness11.532781
Sum-259268.59
Variance37.029394
MonotonicityNot monotonic
2023-08-28T19:46:35.473227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-71.18854061 43
 
1.2%
-71.1898945 42
 
1.1%
-71.1873144 41
 
1.1%
-71.1886346 39
 
1.1%
-71.18744095 30
 
0.8%
-71.18754691 30
 
0.8%
-71.18810467 29
 
0.8%
-71.18833045 29
 
0.8%
-71.18758472 29
 
0.8%
-71.18735268 29
 
0.8%
Other values (124) 3328
90.7%
ValueCountFrequency (%)
-71.1898945 42
1.1%
-71.18987567 28
0.8%
-71.18985575 28
0.8%
-71.18983677 28
0.8%
-71.18981795 27
0.7%
-71.18979938 28
0.8%
-71.18977958 27
0.7%
-71.18975933 29
0.8%
-71.18973907 27
0.7%
-71.18971801 28
0.8%
ValueCountFrequency (%)
0 27
0.7%
-71.18730475 10
 
0.3%
-71.1873144 41
1.1%
-71.18732698 14
 
0.4%
-71.18733866 28
0.8%
-71.18734707 27
0.7%
-71.18735268 29
0.8%
-71.18735783 27
0.7%
-71.18736323 28
0.8%
-71.18736809 27
0.7%

SonarStatus
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0
3669 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3669
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3669
100.0%

Length

2023-08-28T19:46:35.970548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:36.134201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3669
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3669
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3669
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3669
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3669
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3669
100.0%

RangeToFish
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0
3669 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3669
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3669
100.0%

Length

2023-08-28T19:46:36.313615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:36.475772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3669
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3669
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3669
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3669
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3669
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3669
100.0%

BearingToFish
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0
3669 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3669
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3669
100.0%

Length

2023-08-28T19:46:36.650146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:36.818311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3669
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3669
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3669
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3669
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3669
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3669
100.0%

CableOut
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0
3669 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3669
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3669
100.0%

Length

2023-08-28T19:46:36.998708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:37.165553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3669
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3669
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3669
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3669
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3669
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3669
100.0%

Layback
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0.0
3669 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11007
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3669
100.0%

Length

2023-08-28T19:46:37.337429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:37.508322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7338
66.7%
Other Punctuation 3669
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7338
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11007
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

CableTension
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0.0
3669 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11007
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3669
100.0%

Length

2023-08-28T19:46:37.686561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:37.855178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7338
66.7%
Other Punctuation 3669
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7338
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11007
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

SensorDepth
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0.0
3669 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11007
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3669
100.0%

Length

2023-08-28T19:46:38.035389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:38.201397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7338
66.7%
Other Punctuation 3669
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7338
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11007
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

SensorPrimaryAltitude
Real number (ℝ)

HIGH CORRELATION 

Distinct3668
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5691323
Minimum1.6347028
Maximum15.946132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-08-28T19:46:38.376499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.6347028
5-th percentile3.1526895
Q14.0368495
median4.6132317
Q38.5928459
95-th percentile15.318926
Maximum15.946132
Range14.311429
Interquartile range (IQR)4.5559964

Descriptive statistics

Standard deviation3.9010883
Coefficient of variation (CV)0.59385138
Kurtosis-0.058818206
Mean6.5691323
Median Absolute Deviation (MAD)0.75639749
Skewness1.1926327
Sum24102.146
Variance15.21849
MonotonicityNot monotonic
2023-08-28T19:46:38.585483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.962667227 2
 
0.1%
4.037499905 1
 
< 0.1%
5.341619968 1
 
< 0.1%
4.824804306 1
 
< 0.1%
5.21547842 1
 
< 0.1%
4.770983696 1
 
< 0.1%
3.765737772 1
 
< 0.1%
3.786803246 1
 
< 0.1%
4.493227482 1
 
< 0.1%
4.254295826 1
 
< 0.1%
Other values (3658) 3658
99.7%
ValueCountFrequency (%)
1.634702802 1
< 0.1%
1.739616156 1
< 0.1%
1.794777036 1
< 0.1%
1.860962152 1
< 0.1%
1.91167748 1
< 0.1%
1.917957783 1
< 0.1%
1.964471579 1
< 0.1%
2.042103767 1
< 0.1%
2.150669575 1
< 0.1%
2.178627253 1
< 0.1%
ValueCountFrequency (%)
15.94613171 1
< 0.1%
15.93234253 1
< 0.1%
15.90954304 1
< 0.1%
15.88895607 1
< 0.1%
15.85625648 1
< 0.1%
15.82522392 1
< 0.1%
15.79778194 1
< 0.1%
15.79777527 1
< 0.1%
15.77333641 1
< 0.1%
15.766675 1
< 0.1%

SensorAuxAltitude
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0.0
3669 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11007
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3669
100.0%

Length

2023-08-28T19:46:38.778449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:38.915298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7338
66.7%
Other Punctuation 3669
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7338
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11007
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

SensorPitch
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0.0
3669 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11007
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3669
100.0%

Length

2023-08-28T19:46:39.076973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:39.216573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7338
66.7%
Other Punctuation 3669
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7338
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11007
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

SensorRoll
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0.0
3669 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11007
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3669
100.0%

Length

2023-08-28T19:46:39.405133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:39.583549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7338
66.7%
Other Punctuation 3669
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7338
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11007
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

SensorHeading
Real number (ℝ)

HIGH CORRELATION 

Distinct134
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.41506
Minimum0
Maximum175.64999
Zeros27
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-08-28T19:46:39.778914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile72.519997
Q188.730003
median98.879997
Q3115.46
95-th percentile156.03999
Maximum175.64999
Range175.64999
Interquartile range (IQR)26.729996

Descriptive statistics

Standard deviation25.862353
Coefficient of variation (CV)0.24768797
Kurtosis1.7597051
Mean104.41506
Median Absolute Deviation (MAD)12.68
Skewness0.26557849
Sum383098.85
Variance668.86132
MonotonicityNot monotonic
2023-08-28T19:46:40.029227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84.08999634 55
 
1.5%
86.19999695 43
 
1.2%
104.0899963 41
 
1.1%
116.3099976 39
 
1.1%
90.16000366 39
 
1.1%
144.3399963 30
 
0.8%
145.6000061 30
 
0.8%
92.23999786 29
 
0.8%
115.2399979 29
 
0.8%
119.5299988 29
 
0.8%
Other values (124) 3305
90.1%
ValueCountFrequency (%)
0 27
0.7%
39.90999985 28
0.8%
59.40000153 27
0.7%
70.76000214 27
0.7%
71.15000153 28
0.8%
71.54000092 27
0.7%
72.51999664 28
0.8%
76.44000244 27
0.7%
76.61000061 28
0.8%
77.94999695 29
0.8%
ValueCountFrequency (%)
175.6499939 29
0.8%
170.1100006 28
0.8%
166.2899933 27
0.7%
158.7899933 28
0.8%
157.8899994 27
0.7%
156.1300049 28
0.8%
156.0399933 27
0.7%
154.5399933 27
0.7%
152.4199982 28
0.8%
150.8099976 28
0.8%

Heave
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0.0
3669 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11007
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3669
100.0%

Length

2023-08-28T19:46:40.286238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:40.456337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7338
66.7%
Other Punctuation 3669
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7338
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11007
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Yaw
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0.0
3669 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11007
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3669
100.0%

Length

2023-08-28T19:46:40.643377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:40.815632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7338
66.7%
Other Punctuation 3669
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7338
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11007
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7338
66.7%
. 3669
33.3%

AttitudeTimeTag
Real number (ℝ)

HIGH CORRELATION 

Distinct3393
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69288087
Minimum69222359
Maximum69355196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-08-28T19:46:41.027519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum69222359
5-th percentile69227675
Q169254526
median69288066
Q369321638
95-th percentile69348516
Maximum69355196
Range132837
Interquartile range (IQR)67112

Descriptive statistics

Standard deviation38760.661
Coefficient of variation (CV)0.00055941306
Kurtosis-1.201519
Mean69288087
Median Absolute Deviation (MAD)33572
Skewness0.0011663883
Sum2.5421799 × 1011
Variance1.5023889 × 109
MonotonicityIncreasing
2023-08-28T19:46:41.316302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69222359 24
 
0.7%
69222390 13
 
0.4%
69354556 5
 
0.1%
69340516 5
 
0.1%
69332979 4
 
0.1%
69340672 4
 
0.1%
69282357 4
 
0.1%
69343371 4
 
0.1%
69345290 4
 
0.1%
69332838 3
 
0.1%
Other values (3383) 3599
98.1%
ValueCountFrequency (%)
69222359 24
0.7%
69222375 3
 
0.1%
69222390 13
0.4%
69222421 1
 
< 0.1%
69222453 1
 
< 0.1%
69222484 1
 
< 0.1%
69222515 1
 
< 0.1%
69222546 1
 
< 0.1%
69222609 1
 
< 0.1%
69222640 1
 
< 0.1%
ValueCountFrequency (%)
69355196 1
< 0.1%
69355165 1
< 0.1%
69355118 1
< 0.1%
69355087 2
0.1%
69355040 1
< 0.1%
69354978 1
< 0.1%
69354946 1
< 0.1%
69354900 2
0.1%
69354853 1
< 0.1%
69354790 1
< 0.1%

NavFixMilliseconds
Real number (ℝ)

HIGH CORRELATION 

Distinct146
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68778214
Minimum0
Maximum69354884
Zeros27
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-08-28T19:46:41.580973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile69227538
Q169254417
median69287957
Q369321388
95-th percentile69347770
Maximum69354884
Range69354884
Interquartile range (IQR)66971

Descriptive statistics

Standard deviation5922855
Coefficient of variation (CV)0.086115278
Kurtosis131.06526
Mean68778214
Median Absolute Deviation (MAD)33540
Skewness-11.532045
Sum2.5234727 × 1011
Variance3.5080211 × 1013
MonotonicityIncreasing
2023-08-28T19:46:41.846265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69286444 43
 
1.2%
69282466 39
 
1.1%
69338629 30
 
0.8%
69331528 30
 
0.8%
69229566 29
 
0.8%
69305663 29
 
0.8%
69239706 29
 
0.8%
69329500 29
 
0.8%
69344728 29
 
0.8%
69316864 29
 
0.8%
Other values (136) 3353
91.4%
ValueCountFrequency (%)
0 27
0.7%
69222375 15
0.4%
69222468 27
0.7%
69223482 28
0.8%
69224496 28
0.8%
69225510 28
0.8%
69226524 27
0.7%
69227538 28
0.8%
69228552 27
0.7%
69229566 29
0.8%
ValueCountFrequency (%)
69354884 10
 
0.3%
69353854 28
0.8%
69353371 13
0.4%
69352856 14
0.4%
69351826 28
0.8%
69350812 27
0.7%
69349798 29
0.8%
69348784 27
0.7%
69347770 28
0.8%
69346756 27
0.7%

ComputerClockHour
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0
3669 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3669
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3669
100.0%

Length

2023-08-28T19:46:42.082897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:42.259759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3669
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3669
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3669
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3669
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3669
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3669
100.0%

ComputerClockMinute
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0
3669 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3669
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3669
100.0%

Length

2023-08-28T19:46:42.441413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:42.611546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3669
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3669
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3669
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3669
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3669
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3669
100.0%

ComputerClockSecond
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0
3669 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3669
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3669
100.0%

Length

2023-08-28T19:46:42.797582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:42.965110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3669
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3669
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3669
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3669
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3669
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3669
100.0%

ComputerClockHsec
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0
3669 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3669
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3669
100.0%

Length

2023-08-28T19:46:43.143928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:43.317983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3669
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3669
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3669
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3669
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3669
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3669
100.0%

FishPositionDeltaX
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0
3669 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3669
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3669
100.0%

Length

2023-08-28T19:46:43.496880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:43.676643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3669
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3669
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3669
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3669
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3669
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3669
100.0%

FishPositionDeltaY
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0
3669 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3669
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3669
100.0%

Length

2023-08-28T19:46:43.855896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:44.030049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3669
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3669
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3669
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3669
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3669
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3669
100.0%

FishPositionErrorCode
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0
3669 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3669
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3669
100.0%

Length

2023-08-28T19:46:44.216528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:44.394081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3669
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3669
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3669
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3669
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3669
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3669
100.0%

OptionalOffset
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0
3669 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3669
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3669
100.0%

Length

2023-08-28T19:46:44.564707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:44.741414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3669
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3669
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3669
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3669
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3669
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3669
100.0%

CableOutHundredths
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0
3669 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3669
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3669
100.0%

Length

2023-08-28T19:46:44.929958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-28T19:46:45.347064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3669
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3669
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3669
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3669
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3669
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3669
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3669
100.0%

ReservedSpace2
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size28.8 KiB

Interactions

2023-08-28T19:46:12.310487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:22.705144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:26.929063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:30.059840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:33.180321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:36.082821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:39.374662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:42.256053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:45.432880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:48.546420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:51.340233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:54.133292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:57.066874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:00.199419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:03.114324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:06.131371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:09.047116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:12.448347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:23.018406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:27.112532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:30.242328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:33.358162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:36.256381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:39.528553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:42.441227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:45.605127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:48.715660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:51.478003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:54.285100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:57.245986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:00.374984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:03.301707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:06.274851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:09.230978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:12.598362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:23.286817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:27.268833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:30.423604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:33.528541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:36.428677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:39.690921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:42.606406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:45.779349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:48.881059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:51.619857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:54.427737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:57.417025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:00.552219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:03.471506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:06.421323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:09.365676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:12.756728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:23.578968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:27.447510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:30.596886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:33.703311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:36.607265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:39.875773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:42.810910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:45.974032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:49.062941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:51.795352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:54.577538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:57.601496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:00.746458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:03.651976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:06.559410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:09.769531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:12.899904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:23.842810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:27.607569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:30.772748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:33.858293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:36.795094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:40.031497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:42.977806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:46.154717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:49.227400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:51.951911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:54.720154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:57.766531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:00.918916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:03.825645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:06.713138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:09.927604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:13.045755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:24.130467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:27.783649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:30.977134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:34.033553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:36.962236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:40.204877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:43.167834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:46.343691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:49.404166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:52.125564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:54.863418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:57.941556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:01.108278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:04.001398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:06.899029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:10.089874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:13.205315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:24.391462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:27.937679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:31.145532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:34.187627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:37.140293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:40.360018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:43.339196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:46.501348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:49.562446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:52.278521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:55.041178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:58.097045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:01.277306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:04.170842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:07.066847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:10.245148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:13.380091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:24.702631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:28.110516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:31.340075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:34.362877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:37.324521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:40.524051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:43.554305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:46.676586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:49.738869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:52.455503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:55.200991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:58.282876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:01.462285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:04.359163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:07.254488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:10.429208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:13.573963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:25.050410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:28.303112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:31.514443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:34.534522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:37.514549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:40.714021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:43.753445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:46.840364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:49.919266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:52.625667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:55.390304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:58.473381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:01.655065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:04.544746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:07.439385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:10.640754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:13.756366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:25.311870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:28.503354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:31.709434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:34.708749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:37.693607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:40.878692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:43.945816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:46.979434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:50.086019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:52.788764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:55.576944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:58.677327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:01.832495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:04.718929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:07.612766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:10.833871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:13.929399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:25.523784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:28.666862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:31.878383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:34.863618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:37.853902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:41.016628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:44.143281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:47.131176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:50.254072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:52.940272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:55.745098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:58.828150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:01.997766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:04.895059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:07.781536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:11.005403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:14.093390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:25.686009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:28.990707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:32.048604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:35.013092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:38.038441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:41.191584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:44.314032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:47.274230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:50.423400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:53.098001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:55.899703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:58.974514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:02.149573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:05.050766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:07.948992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:11.189120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:14.264006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:25.879544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:29.156478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:32.216913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:35.181952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:38.206437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:41.345103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:44.482917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:47.413154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:50.579291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:53.252077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:56.065073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:59.107587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:02.301140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:05.217215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:08.115230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:11.361784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:14.447113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:26.078285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:29.342576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:32.407608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:35.365537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:38.627405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:41.522054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:44.676065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:47.563185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:50.741951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:53.428651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:56.261596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:59.261558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:02.459210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:05.419906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:08.298001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:11.548825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:14.640622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:26.274128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:29.524315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:32.602131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:35.545017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:38.805197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:41.697433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:44.868614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:47.742437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:50.890599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:53.605119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:56.455126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:59.676296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:02.601403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:05.595871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:08.481579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:11.758883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:14.831191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:26.475721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:29.697095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:32.787761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:35.720125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:38.977508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:41.874850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:45.049986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:47.926127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:51.040903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:53.780584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:56.654610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:59.844261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:02.773302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:05.756042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:08.670053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:11.947115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:15.031507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:26.728342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:29.884018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:32.988568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:35.910896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:39.176739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:42.048585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:45.243902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:48.366363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:51.191725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:53.958637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:45:56.876054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:00.028252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:02.927139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:05.929147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:08.852166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-28T19:46:12.129422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-08-28T19:46:45.486629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SecondHSecondsPingNumberShipSpeedShipGyroShipYcoordinateShipXcoordinateShipAltitudeFixTimeSecondFixTimeHsecondSensorSpeedSensorYcoordinateSensorXcoordinateSensorPrimaryAltitudeSensorHeadingAttitudeTimeTagNavFixMillisecondsMinuteFixTimeHourFixTimeMinute
Second1.0000.0050.037-0.0710.002-0.2470.050-0.0230.925-0.183-0.071-0.2470.050-0.0240.0020.0370.0370.3830.2060.320
HSeconds0.0051.0000.0120.0090.007-0.0000.001-0.0000.015-0.0820.009-0.0000.001-0.0000.0070.0120.0140.0380.2440.137
PingNumber0.0370.0121.0000.1570.800-0.8600.9560.8500.0460.1470.157-0.8600.9560.8500.8001.0001.0000.9280.2540.765
ShipSpeed-0.0710.0090.1571.0000.220-0.0210.1130.044-0.060-0.1861.000-0.0210.1130.0440.2200.1570.1570.4270.9990.669
ShipGyro0.0020.0070.8000.2201.000-0.6660.7560.707-0.0020.1610.220-0.6660.7560.7071.0000.8000.8000.6140.9990.763
ShipYcoordinate-0.247-0.000-0.860-0.021-0.6661.000-0.904-0.753-0.204-0.022-0.0211.000-0.904-0.753-0.666-0.860-0.8600.2100.9811.000
ShipXcoordinate0.0500.0010.9560.1130.756-0.9041.0000.8260.0020.1020.113-0.9041.0000.8270.7560.9560.9560.2100.9811.000
ShipAltitude-0.023-0.0000.8500.0440.707-0.7530.8261.000-0.0230.0560.044-0.7530.8260.9990.7070.8500.8500.6450.0970.532
FixTimeSecond0.9250.0150.046-0.060-0.002-0.2040.002-0.0231.000-0.158-0.060-0.2040.002-0.024-0.0020.0460.0460.3620.2560.353
FixTimeHsecond-0.183-0.0820.147-0.1860.161-0.0220.1020.056-0.1581.000-0.186-0.0220.1020.0560.1610.1470.1470.3140.9990.618
SensorSpeed-0.0710.0090.1571.0000.220-0.0210.1130.044-0.060-0.1861.000-0.0210.1130.0440.2200.1570.1570.4270.9990.669
SensorYcoordinate-0.247-0.000-0.860-0.021-0.6661.000-0.904-0.753-0.204-0.022-0.0211.000-0.904-0.753-0.666-0.860-0.8600.2100.9811.000
SensorXcoordinate0.0500.0010.9560.1130.756-0.9041.0000.8260.0020.1020.113-0.9041.0000.8270.7560.9560.9560.2100.9811.000
SensorPrimaryAltitude-0.024-0.0000.8500.0440.707-0.7530.8270.999-0.0240.0560.044-0.7530.8271.0000.7070.8500.8500.6370.0920.524
SensorHeading0.0020.0070.8000.2201.000-0.6660.7560.707-0.0020.1610.220-0.6660.7560.7071.0000.8000.8000.6140.9990.763
AttitudeTimeTag0.0370.0121.0000.1570.800-0.8600.9560.8500.0460.1470.157-0.8600.9560.8500.8001.0001.0000.9310.2400.763
NavFixMilliseconds0.0370.0141.0000.1570.800-0.8600.9560.8500.0460.1470.157-0.8600.9560.8500.8001.0001.0000.2100.9811.000
Minute0.3830.0380.9280.4270.6140.2100.2100.6450.3620.3140.4270.2100.2100.6370.6140.9310.2101.0000.2100.983
FixTimeHour0.2060.2440.2540.9990.9990.9810.9810.0970.2560.9990.9990.9810.9810.0920.9990.2400.9810.2101.0001.000
FixTimeMinute0.3200.1370.7650.6690.7631.0001.0000.5320.3530.6180.6691.0001.0000.5240.7630.7631.0000.9831.0001.000

Missing values

2023-08-28T19:46:15.481339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-28T19:46:16.125856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

MagicNumberHeaderTypeSubChannelNumberNumChansToFollowReserved1NumBytesThisRecordYearMonthDayHourMinuteSecondHSecondsJulianDayEventNumberPingNumberSoundVelocityWaterTemperaturePressureComputedSoundVelocityMagXMagYMagZAuxVal1AuxVal2AuxVal3AuxVal4AuxVal5AuxVal6SpeedLogTurbidityShipSpeedShipGyroShipYcoordinateShipXcoordinateShipAltitudeShipDepthFixTimeHourFixTimeMinuteFixTimeSecondFixTimeHsecondSensorSpeedSensorYcoordinateSensorXcoordinateSonarStatusRangeToFishBearingToFishCableOutLaybackCableTensionSensorDepthSensorPrimaryAltitudeSensorAuxAltitudeSensorPitchSensorRollSensorHeadingHeaveYawAttitudeTimeTagNavFixMillisecondsComputerClockHourComputerClockMinuteComputerClockSecondComputerClockHsecFishPositionDeltaXFishPositionDeltaYFishPositionErrorCodeOptionalOffsetCableOutHundredthsReservedSpace2
064206002[0, 0]238420207241913423520600737.50.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.040000000.00.00.000000.00.00.04.0375000.00.00.00.00.00.0692223590000000000[0, 0, 0, 0, 0, 0]
164206002[0, 0]238420207241913423520601737.50.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.040000000.00.00.000000.00.00.04.0406250.00.00.00.00.00.0692223590000000000[0, 0, 0, 0, 0, 0]
264206002[0, 0]238420207241913423520602737.50.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.040000000.00.00.000000.00.00.04.0398440.00.00.00.00.00.0692223590000000000[0, 0, 0, 0, 0, 0]
364206002[0, 0]238420207241913423520603737.50.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.040000000.00.00.000000.00.00.04.0455080.00.00.00.00.00.0692223590000000000[0, 0, 0, 0, 0, 0]
464206002[0, 0]238420207241913423520604737.50.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.040000000.00.00.000000.00.00.04.0466310.00.00.00.00.00.0692223590000000000[0, 0, 0, 0, 0, 0]
564206002[0, 0]238420207241913423520605737.50.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.040000000.00.00.000000.00.00.04.0474730.00.00.00.00.00.0692223590000000000[0, 0, 0, 0, 0, 0]
664206002[0, 0]238420207241913423520606737.50.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.041000000.00.00.000000.00.00.04.0512300.00.00.00.00.00.0692223590000000000[0, 0, 0, 0, 0, 0]
764206002[0, 0]238420207241913423520607737.50.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.041000000.00.00.000000.00.00.04.0540480.00.00.00.00.00.0692223590000000000[0, 0, 0, 0, 0, 0]
864206002[0, 0]238420207241913423520608737.50.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.041000000.00.00.000000.00.00.04.0592860.00.00.00.00.00.0692223590000000000[0, 0, 0, 0, 0, 0]
964206002[0, 0]238420207241913423520609737.50.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.041000000.00.00.000000.00.00.04.0600890.00.00.00.00.00.0692223590000000000[0, 0, 0, 0, 0, 0]
MagicNumberHeaderTypeSubChannelNumberNumChansToFollowReserved1NumBytesThisRecordYearMonthDayHourMinuteSecondHSecondsJulianDayEventNumberPingNumberSoundVelocityWaterTemperaturePressureComputedSoundVelocityMagXMagYMagZAuxVal1AuxVal2AuxVal3AuxVal4AuxVal5AuxVal6SpeedLogTurbidityShipSpeedShipGyroShipYcoordinateShipXcoordinateShipAltitudeShipDepthFixTimeHourFixTimeMinuteFixTimeSecondFixTimeHsecondSensorSpeedSensorYcoordinateSensorXcoordinateSonarStatusRangeToFishBearingToFishCableOutLaybackCableTensionSensorDepthSensorPrimaryAltitudeSensorAuxAltitudeSensorPitchSensorRollSensorHeadingHeaveYawAttitudeTimeTagNavFixMillisecondsComputerClockHourComputerClockMinuteComputerClockSecondComputerClockHsecFishPositionDeltaXFishPositionDeltaYFishPositionErrorCodeOptionalOffsetCableOutHundredthsReservedSpace2
365964206002[0, 0]238420207241915549020603659737.50.00.00.00.00.00.00.00.00.00.00.00.00.00.02.43144.24000546.835499-71.187305860191554882.4346.835499-71.18730500000.00.00.08.5501390.00.00.0144.2400050.00.06935490069354884000000000[0, 0, 0, 0, 0, 0]
366064206002[0, 0]238420207241915549020603660737.50.00.00.00.00.00.00.00.00.00.00.00.00.00.02.43144.24000546.835499-71.187305840191554882.4346.835499-71.18730500000.00.00.08.4344790.00.00.0144.2400050.00.06935490069354884000000000[0, 0, 0, 0, 0, 0]
366164206002[0, 0]238420207241915549420603661737.50.00.00.00.00.00.00.00.00.00.00.00.00.00.02.43144.24000546.835499-71.187305830191554882.4346.835499-71.18730500000.00.00.08.3414840.00.00.0144.2400050.00.06935494669354884000000000[0, 0, 0, 0, 0, 0]
366264206002[0, 0]238420207241915549720603662737.50.00.00.00.00.00.00.00.00.00.00.00.00.00.02.43144.24000546.835499-71.187305830191554882.4346.835499-71.18730500000.00.00.08.2779880.00.00.0144.2400050.00.06935497869354884000000000[0, 0, 0, 0, 0, 0]
366364206002[0, 0]23842020724191555420603663737.50.00.00.00.00.00.00.00.00.00.00.00.00.00.02.43144.24000546.835499-71.187305820191554882.4346.835499-71.18730500000.00.00.08.2272420.00.00.0144.2400050.00.06935504069354884000000000[0, 0, 0, 0, 0, 0]
366464206002[0, 0]23842020724191555820603664737.50.00.00.00.00.00.00.00.00.00.00.00.00.00.02.43144.24000546.835499-71.187305820191554882.4346.835499-71.18730500000.00.00.08.1923060.00.00.0144.2400050.00.06935508769354884000000000[0, 0, 0, 0, 0, 0]
366564206002[0, 0]23842020724191555820603665737.50.00.00.00.00.00.00.00.00.00.00.00.00.00.02.43144.24000546.835499-71.187305820191554882.4346.835499-71.18730500000.00.00.08.1692300.00.00.0144.2400050.00.06935508769354884000000000[0, 0, 0, 0, 0, 0]
366664206002[0, 0]238420207241915551120603666737.50.00.00.00.00.00.00.00.00.00.00.00.00.00.02.43144.24000546.835499-71.187305820191554882.4346.835499-71.18730500000.00.00.08.1706720.00.00.0144.2400050.00.06935511869354884000000000[0, 0, 0, 0, 0, 0]
366764206002[0, 0]238420207241915551620603667737.50.00.00.00.00.00.00.00.00.00.00.00.00.00.02.43144.24000546.835499-71.187305820191554882.4346.835499-71.18730500000.00.00.08.1655040.00.00.0144.2400050.00.06935516569354884000000000[0, 0, 0, 0, 0, 0]
366864206002[0, 0]238420207241915551920603668737.50.00.00.00.00.00.00.00.00.00.00.00.00.00.02.43144.24000546.835499-71.187305820191554882.4346.835499-71.18730500000.00.00.08.1553780.00.00.0144.2400050.00.06935519669354884000000000[0, 0, 0, 0, 0, 0]